def test_readme(self):
environment = UnittestEnvironment(states=dict(type='float',
shape=(10, )),
actions=dict(type='int',
num_values=5))
def get_current_state():
return environment.reset()
def execute_decision(x):
return environment.execute(actions=x)[2]
# Instantiate a Tensorforce agent
agent = PPOAgent(states=dict(type='float', shape=(10, )),
actions=dict(type='int', num_values=5),
memory=10000,
network='auto',
update_mode=dict(unit='episodes', batch_size=10),
step_optimizer=dict(type='adam', learning_rate=1e-4))
# Initialize the agent
agent.initialize()
# Retrieve the latest (observable) environment state
state = get_current_state() # (float array of shape [10])
# Query the agent for its action decision
action = agent.act(states=state) # (scalar between 0 and 4)
# Execute the decision and retrieve the current performance score
reward = execute_decision(action) # (any scalar float)
# Pass feedback about performance (and termination) to the agent
agent.observe(reward=reward, terminal=False)
agent.close()
environment.close()
self.assertTrue(expr=True)
# - format: 'numpy' or 'hdf5' store only weights, 'checkpoint' stores full TensorFlow model
runner.agent.save(directory="C:\\Users\\ali_k\\Desktop\\my_model",
format='checkpoint')
# Print resulting stats and graphs
print("Stats: ", runner.episode_rewards, runner.episode_timesteps)
episode_rewards, episode_timesteps = runner.episode_rewards, runner.episode_timesteps
plt.plot(runner.episode_rewards, label='reward')
plt.hist(runner.episode_rewards, label='reward')
plt.plot(runner.episode_timesteps, label='life time')
plt.hist(runner.episode_timesteps, label='life time')
try:
agent.close()
runner.close()
except AttributeError as e:
print("Hata:", e)
pass
# Test the agent with RandomAgent opponents
test_agents = []
for agent_id in range(3):
test_agents.append(
SimpleAgent(config["agent"](agent_id, config["game_type"])))
# Add TensorforceAgent
agent_id += 1
test_agents.append(